4.5 Article

A Boosted Communicational Salp Swarm Algorithm: Performance Optimization and Comprehensive Analysis

期刊

JOURNAL OF BIONIC ENGINEERING
卷 20, 期 3, 页码 1296-1332

出版社

SPRINGER SINGAPORE PTE LTD
DOI: 10.1007/s42235-022-00304-y

关键词

Salp swarm algorithm; Swarm intelligence; Global optimization; Exploration; Exploitation

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This paper proposes an improved algorithm named RCSSSA based on SSA, which enhances the convergence accuracy and speed by adding real-time update mechanism, communication strategy, and selective replacement strategy. Experimental results demonstrate that RCSSSA can converge faster and achieve better optimization results compared to traditional swarm intelligence and other improved algorithms.
The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.

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